Method and apparatus for accurate localization of points of interest

ABSTRACT

Geo-referenced and oriented media items may be used to determine a location of one or more points of interest depicted by the media items. A difference between an actual capture location and orientation and a reported location and orientation may be modeled according to one or more distributions, which distribution(s) may be used to assign one or more weights to each location in the world where such weight(s) may be considered to be a likelihood that a point of interest might have been seen by a capturing device. A density map may be acquired by superimposing the derived likelihoods, and a maximum, e.g., local maximum, may be determined to represent a location of a point of interest.

FIELD OF THE DISCLOSURE

The present disclosure relates to geographic locations of points ofinterest, and more particularly to determining a geographic location ofa point of interest using georeferenced, or geotagged, and orientedmedia items.

BACKGROUND

In the last few years digital media capturing devices, such as photocameras and mobile phones, have increasingly been equipped with globalposition system (GPS) chips, enabling media items, such as withoutlimitation photographs, videos, etc., to be associated with a geographiclocation. Such georeferenced, or geotagged, digital media areincreasingly forming part of the media items, e.g., photographs, stillimages, video, etc., being taken, uploaded and shared. Nonetheless, thelocation associated with such a media item refers to the location atwhich it was captured, i.e., the location of the digital media capturingdevice, rather than the location(s) of a point of interest (POI)depicted in the media item.

SUMMARY

The present disclosure seeks to address failings in the art and to usecapture information including location and orientation information,which information may be provided by the digital media capturingdevice(s), to determine or estimate a location of an object that isdepicted in the media item. In accordance with one or more embodiments,capture information including location and orientation information,which orientation information may be used relative to true or magneticnorth and may be captured at the time that the media item is captured,may be used to accurately estimate the location of a depicted object,e.g., a POI.

In accordance with one or more embodiments, georeferenced and orientedmedia items taken close to each other may be accumulated and a mostlikely geographic location of a point of interest may be inferred bytracing the lines of sight originating from the capturing device'slocation along its orientation. Measurement inaccuracies associated withboth geo-location and geo-orientation may be accounted for during alocation estimation process. More specifically and in accordance withone or more embodiments, a difference between an actual capture locationand orientation, which location and orientation may be associated withany point in the world, and a reported location and orientation can bemodeled according to a distribution, such as and without limitation abivariate Weibull, Rayleigh, Gamma or Gaussian distribution, and eachlocation in the world may be weighed by the likelihood it could havebeen seen by the camera. By doing this for a plurality of media itemsand superimposing derived likelihoods, a density map may be generatedwhere each local maximum surpassing some threshold can be considered apoint of interest. In accordance with one or more embodiments, morepopular points of interest may have more support than lesser points ofinterest.

In accordance with one or more embodiments, a method is provided, themethod comprising selecting, using at least one computing system, aplurality of digital media items, each media item of the pluralityhaving associated capture information identifying a location andorientation of a digital media device capturing the media item;generating, using the at least one computing system, a plurality ofweights for each media item in the plurality, each weight of the mediaitem's plurality of weights corresponding to a geographic location of aplurality of geographic locations, the weight reflects an estimatedinaccuracy in the identified location and orientation of the digitalmedia device and is generated using information including the mediaitem's identified location and orientation, the generated weight for usein determining whether a point of interest depicted in the media item islocated at the geographic location; and for each geographic location ofthe plurality, aggregating, using the at least one computing system, theplurality of weights generated for the geographic location in connectionwith each media item of the plurality of media items, an aggregatedweight for a given geographic location identifying a probability that atleast one point of interest is located at the given geographic location.

In accordance with one or more embodiments a system is provided, whichsystem comprises at least one computing device comprising one or moreprocessors to execute and memory to store instructions to select aplurality of digital media items, each media item of the pluralityhaving associated capture information identifying a location andorientation of a digital media device capturing the media item; generatea plurality of weights for each media item in the plurality, each weightof the media item's plurality of weights corresponding to a geographiclocation of a plurality of geographic locations, the weight reflects anestimated inaccuracy in the identified location and orientation of thedigital media device and is generated using information including themedia item's identified location and orientation, the generated weightfor use in determining whether a point of interest depicted in the mediaitem is located at the geographic location; and for each geographiclocation of the plurality, aggregating, use the at least one computingsystem, the plurality of weights generated for the geographic locationin connection with each media item of the plurality of media items, anaggregated weight for a given geographic location identifying aprobability that at least one point of interest is located at the givengeographic location.

In accordance with yet another aspect of the disclosure, a computerreadable non-transitory storage medium is provided, the medium fortangibly storing thereon computer readable instructions that whenexecuted cause at least one processor to select a plurality of digitalmedia items, each media item of the plurality having associated captureinformation identifying a location and orientation of a digital mediadevice capturing the media item; generate a plurality of weights foreach media item in the plurality, each weight of the media item'splurality of weights corresponding to a geographic location of aplurality of geographic locations, the weight reflects an estimatedinaccuracy in the identified location and orientation of the digitalmedia device and is generated using information including the mediaitem's identified location and orientation, the generated weight for usein determining whether a point of interest depicted in the media item islocated at the geographic location; and for each geographic location ofthe plurality, aggregating, use the at least one computing system, theplurality of weights generated for the geographic location in connectionwith each media item of the plurality of media items, an aggregatedweight for a given geographic location identifying a probability that atleast one point of interest is located at the given geographic location.

In accordance with one or more embodiments, a system is provided thatcomprises one or more computing devices configured to providefunctionality in accordance with such embodiments. In accordance withone or more embodiments, functionality is embodied in steps of a methodperformed by at least one computing device. In accordance with one ormore embodiments, program code to implement functionality in accordancewith one or more such embodiments is embodied in, by and/or on acomputer-readable medium.

DRAWINGS

The above-mentioned features and objects of the present disclosure rillbecome more apparent with reference to the following description takenin conjunction with the accompanying drawings wherein like referencenumerals denote like elements and in which:

FIG. 1 provides an example of an intersection of multiple lines of sightassociated with multiple digital media capturing devices.

FIG. 2 provides an example of multiple intersection points that mayoccur where location and orientation measurements provided by multipledigital media capturing devices are imprecise.

FIG. 3 provides an example of an isosceles triangle formed in accordancewith one or more embodiments.

FIG. 4 illustrates capture orientation modeling in accordance with oneor more embodiments of the present disclosure.

FIG. 5 illustrates capture location modeling in accordance with one ormore embodiments of the present disclosure.

FIG. 6 provides an example of a modeling of the convolved weights inaccordance with one or more embodiments of the present disclosure.

FIG. 7 provides an example of a density mapping formed from aggregatedconvolved weights associated with a number of photos in accordance withone or more embodiments of the present disclosure.

FIG. 8, which comprises FIGS. 8A and 8B, provides an example processflow to assign weight(s) to world locations in accordance with one ormore embodiments of the present disclosure.

FIG. 9, which comprises FIGS. 9A and 9B, provides process flow examplesfor use in determining capture orientation and location inaccuracyweights and density weights in accordance with one or more embodiments.

FIG. 10 provides a weight aggregation process flow in accordance withone or more embodiments of the present disclosure.

FIG. 11 provides examples of flat and Gaussian kernels in accordancewith one or more embodiments of the present disclosure.

FIG. 12 illustrates some components that can be used in connection withone or more embodiments of the present disclosure.

FIG. 13 is a detailed block diagram illustrating an internalarchitecture of a computing device in accordance with one or moreembodiments of the present disclosure.

DETAILED DESCRIPTION

Subject matter will now be described more fully hereinafter withreference to the accompanying drawings, which form a part hereof, andwhich show, by way of illustration, specific example embodiments.Subject matter may, however, be embodied in a variety of different formsand, therefore, covered or claimed subject matter is intended to beconstrued as not being limited to any example embodiments set forthherein; example embodiments are provided merely to be illustrative.Likewise, a reasonably broad scope for claimed or covered subject matteris intended. Among other things, for example, subject matter may beembodied as methods, devices, components, or systems. Accordingly,embodiments may, for example, take the form of hardware, software,firmware or any combination thereof (other than software per se). Thefollowing detailed description is, therefore, not intended to be takenin a limiting sense.

Throughout the specification and claims, terms may have nuanced meaningssuggested or implied in context beyond an explicitly stated meaning.Likewise, the phrase “in one embodiment” as used herein does notnecessarily refer to the same embodiment and the phrase “in anotherembodiment” as used herein does not necessarily refer to a differentembodiment. It is intended, for example, that claimed subject matterinclude combinations of example embodiments in whole or in part.

In general, terminology may be understood at least in part from usage incontext. For example, terms, such as “and”, “or”, or “and/or,” as usedherein may include a variety of meanings that may depend at least inpart upon the context in which such terms are used. Typically, “or” ifused to associate a list, such as A, B or C, is intended to mean A, B,and C, here used in the inclusive sense, as well as A, B or C, here usedin the exclusive sense. In addition, the term “one or more” as usedherein, depending at least in part upon context, may be used to describeany feature, structure, or characteristic in a singular sense or may beused to describe combinations of features, structures or characteristicsin a plural sense. Similarly, terms, such as “a,” “an,” or “the,” again,may be understood to convey a singular usage or to convey a pluralusage, depending at least in part upon context. In addition, the term“based on” may be understood as not necessarily intended to convey anexclusive set of factors and may, instead, allow for existence ofadditional factors not necessarily expressly described, again, dependingat least in part on context.

The detailed description provided herein is not intended as an extensiveor detailed discussion of known concepts, and as such, details that areknown generally to those of ordinary skill in the relevant art may havebeen omitted or may be handled in summary fashion. Certain embodimentsof the present disclosure will now be discussed with reference to theaforementioned figures, wherein like reference numerals refer to likecomponents.

In general, the present disclosure includes a system, method andarchitecture for accurate estimation of geo-location and geo-orientationfor points of interest using georeferenced and oriented media itemsdepicting the points of interest.

In accordance with one or more embodiments, a compass direction suppliedby the digital media capturing device may be used to compute theintersections between the lines of sight originating from the devicesfocusing on the same scene, so that the locations of the points ofinterest depicted in the scene may be predicted. The accuracy of thedevices that supply the geographic coordinates and the compass directionmay vary and these imprecision's may be taken into account whenestimating a location.

In accordance with one or more embodiments, a geographic location of aPOI depicted in a media item may be estimated using the location andorientation information, which information may be supplied by thedevice(s) that captured the media item. By computing the intersectionsbetween the lines of sight from multiple devices taken of the samescene, a reliable location and orientation estimate may be made. FIG. 1provides an example of an intersection of multiple lines of sightassociated with multiple digital media capturing devices in accordancewith one or more embodiments. In the example of FIG. 1, devices102A-102F each capture the media item and geo-positioning informationfor each of the devices 102A-102F is captured and used in identifying aline of sight, e.g., lines of sight 104A-104F corresponding to devices102A-102F, respectively. The lines of sight 102A-102F intersect atintersection point 106, intersection point 106 may be considered to be alocation of a point of interest depicted in the media items captured bythe devices 102A-102F. The lines of sight 102A-102F shown in FIG. 1correspond to accurate and precise location and orientation measurementsprovided by devices 102A-102F; however, not all devices provide accurateand precise measurements which impacts the determination of a locationof a POI.

FIG. 2 provides an example of multiple intersection points that mayoccur where location and orientation measurements provided for multipledigital media capturing devices are imprecise. In the example of FIG. 2,the accuracy of the devices 102A-102F that supply the longitude andlatitude coordinates and the compass direction may vary depending onsuch factors as manufacturing quality, structural interference,atmospheric conditions and signal reception. Consequently, theintersections of the lines of sight 204A-204F of the devices 102A-120Fmay not necessarily coincide. In the example of FIG. 2, there aremultiple intersections, such as intersections 208AC, 208CD and 208BF,which may result from the lack of coincidence of the lines of sight204A-204F. In the example shown in FIG. 2, POI 206 is exemplified by theshaded square, such that the location of the POI 206 is assumed to be inthe center of the square and the spatial footprint of the POIcorresponds to the size of the square. In some cases, a POI may notnecessarily be located in the center of the media item. That is, a lineof sight typically points where the device points, and the device maynot be pointing directly at the point(s) of interest captured by thedevice; consequently, the point(s) of interest may not be located alongthe line of sight of the device.

Increasingly, mobile phones are being used as cameras. Mobile phoneshave been shown to provide inaccurate positioning information. Even whenoutfitted with GPS, it is not clear whether the time which a photo istaken by mobile phone is sufficient to obtain reliable GPS signal, andhence a reliable location estimate. There may be inaccuracies in compassorientation information provided by digital media capturing devices,including mobile phones, in addition to positioning inaccuracies.

In accordance with one or more embodiments, longitude and latitudemeasurement error (s) may be approximated, or modeled, using aprobability, or statistical, distribution, such as and withoutlimitation a bivariate Weibull, Rayleigh, Gamma or Gaussiandistribution.

When a sufficient number of photographs have been taken of the same POI,the displacements of the POIs in an individual photo from the center ofthe image may effectively be canceled out, such that particular measuresto address displacements need not be taken into account. Embodiments ofthe present disclosure may take the displacement(s) of POI(s) intoaccount by, for example, using visual matching, which is discussed inmore detail below.

Embodiments of the present disclosure are described herein withreference to photos, or photographs; however it should be apparent thatembodiments of the present disclosure are applicable to any type ofmedia item, including any media item produced by any type of digitalmedia capturing device now known or later developed.

For purposes of discussion herein, a collection of photos may bereferred to as P, where each photo of the collection may be referred toas pεP, and may be represented by a tuple (u_(p),θ_(p)), where u_(p) maybe the capture location of the photo, which capture location may beexpressed as u_(p)=(λ_(p),φ_(p)) with longitude λ_(p) and latitude,φ_(p), and θ_(p) may be the capture orientation of the photo which maybe measured anti-clockwise from true north. The tuple may be included incapture information for a device used to capture a photo. Additionally,the geographic distance between any two locations u_(a) and u_(b) may bereferred to as δ(u_(a),u_(b)) and may be measured in metric units.

Thus, any point u_(w) in the world may be expressed relative to acapture location u_(p) of a photo in terms of its geographic distanceδ(u_(w),u_(p)) and in terms of the orientation angle, which may beexpressed as an angle, α(u_(w), u_(p),θ_(p)), which is an angle that isrelative to the capture orientation of the line spanned between thecapture location and the point in the world, and may be expressed as:

$\begin{matrix}{{{\alpha\left( {u_{w},u_{p},\theta_{p}} \right)} = {180^{{^\circ}} - {2\beta}}},{where}} & {{Expr}.\mspace{11mu}(1)} \\{{\beta = {\arccos\left( \frac{\delta\left( {u_{w},u_{o}} \right)}{2{\delta\left( {u_{w},u_{p}} \right)}} \right)}},} & {{Expr}.\mspace{11mu}(2)}\end{matrix}$

In expression (2), a point, u_(o) represents a position along a capturedevice's line of sight such that a distance, also referred to as a firstdistance, between a point in the world, u_(w), and a capture location,u_(p) which first distance may be expressed as δ(u_(w),u_(p)), is equalto a distance, also referred to as a second distance, between thecapture location and the position along the capture device's line ofsight, which second distance may be expressed as δ(u_(o),u_(p)), andsuch equality between the first and second distances may expressed asδ(u_(w),u_(p))=δ(u_(o),u_(p)). The line of sight point u_(o) may beexpressed in terms of its longitude λ_(o) and latitude, φ_(o), asfollows:u _(o)=(λ_(o),φ_(o))λ_(o)=λ_(p)−δ(u _(w) ,u _(p))sin θ_(p)φ_(o)=φ_(p)+δ(u _(w) ,u _(p))cos θ_(p)  Expr. (3)

Thus, in accordance with one or more embodiments, a point along the lineof sight, which point may be expressed as u_(o)=(λ_(o),φ_(o)), may beselected such that the distance between the capture and world points andthe distance between the capture and line of sight points are equal, orδ(u_(w),u_(p))=δ(u_(o),u_(p)). An isosceles triangle may be formed usingu_(p), u_(w) and u_(o) where two of the triangle's angles are equal toβ, where β may be determined using expression (2), and the third angleis equal to α, where α may be determined using expression (1).

FIG. 3 provides an example of an isosceles triangle formed in accordancewith one or more embodiments. In the example of FIG. 3, triangle 300 isan isosceles triangle formed from world, capture and line of sightpoints 302, 304 and 306 (respectively), which form the vertices oftriangle 300, and sides 312, 314 and 316 are formed between thevertices. The line of sight point 306, or u_(o), is selected such thatsides 314 and 316 are equal in geographic distance, e.g., the geographicdistances represented by the sides 314 and 316 are equal, and angles 322and 324 are equal to β, which may be determined using expression (2).Given a selected world point, u_(w)=(λ_(w),φ_(w)), a capture point,u_(p)=(λ_(p),φ_(p)), and a capture orientation, θ_(p), which ispreferably measured anti-clockwise from true north, a geographicdistance, δ(u_(o),u_(p)), or second distance, and the orientation angle,α(u_(w), u_(p),θ_(p)), may be determined. In accordance with one or moreembodiments, the orientation angle α(u_(w), u_(p),θ_(p)) may bedetermined using expression (1). Notably, when a line of sight point isselected that is the same as the selected world point, no triangle isformed, but expression (1) may be used and yields a value of zero forthe third angle, or α=0.

In accordance with one or more embodiments, a selected world point u_(w)may be any point in the world. Alternatively, a selected world pointu_(w) may be within a certain distance of the capture point u_(p). Inthe example shown in FIG. 3, a geographic area is defined by a circlethat has its center at the capture point u_(p) and the world point u_(w)is within the circle. A portion 320 of the circle indicating ageographic area is shown in FIG. 3.

It should be apparent that an area other than a circular area may beused to define a geographic area from which a world point is selected.The area may have a non-uniform shape. The area that is used may takeinto account the curvature of the earth and other elements such asbuildings and hills, which may cause points of interest to be out ofsight from a given capture point. Points of interest may often belocated in densely populated areas with many other surrounding buildingsblocking views from far away. In accordance with one or moreembodiments, it might be assumed that each photo pεP of the collectionof photos P was taken within a certain distance, such as and withoutlimitation 2.5 km, of a point of interest captured by the photo. Itshould be apparent that any distance may be used. By way of anon-limiting example, the tallest building in the world, currently theBurj Kalifa in Dubai at 829.8 m, can be seen from about 100 km away on aclear day.

As discussed herein, POIs are, on average, positioned roughly in thecenter of the photographs of the POIs, and therefore is not necessary toexplicitly account for the POIs not being depicted in the center of anyindividual photographs. Furthermore, given a sufficient number of photostaken of the same point of interest, the displacement of a point ofinterest from the center of the image in each individual photo mayeffectively be canceled by the displacement(s) introduced by one or moreother photos.

Embodiments of the present disclosure take into account inaccuracies incapture location and orientation measurements reported by, or for, thecapturing device(s). In accordance with one or more embodiments, suchinaccuracies may follow a distribution, such as a Gaussian distribution,with respect to true, or actual, measurements, and the true, or actual,capture location and orientation of the media items may be modeled bysmoothing the reported location and orientation measurements with one ormore Gaussian distributions. It should be apparent that otherdistributions, such as and without limitation a Weibull, Rayleigh,Gamma, etc. distribution, may be used in connection with one or moreembodiments of the present disclosure.

In accordance with one or more embodiments, the difference between theactual capture location and orientation and the reported location andorientation may be modeled according to a distribution, such as andwithout limitation a Weibull, Rayleigh, Gamma or Gaussian distribution.In accordance with one or more such embodiments, each location in theworld may be weighted by the likelihood that it could have been seen bya capturing device. By doing this for all media items and superimposingderived likelihoods, a density map may be acquired where each localmaximum surpassing some threshold can be considered a POI, and the morepopular POIs may have more support relative to the lesser POIs.

In accordance with one or more embodiments, orientation accuracy may bemodeled using a one-dimensional Gaussian distribution with standarddeviation σ_(θ) and centered on θ_(p), where σ_(θ) may represent afactor that determines how quickly an influence drops. The Gaussiandistribution model that may be used to address orientation inaccuracymay be expressed as:

$\begin{matrix}{{G_{\theta_{p}}\left( {{u_{w};u_{p}},\theta_{p},\sigma_{\theta}} \right)} = {\frac{1}{\sqrt{2\pi}\sigma_{\theta}}{\mathbb{e}}^{- \frac{{\alpha{({u_{w},u_{p},\theta_{p}})}}^{2}}{2\sigma_{\theta}^{2}}}}} & {{Expr}.\mspace{11mu}(4)}\end{matrix}$

In accordance with one or more embodiments, expression (4) may be usedto address orientation inaccuracy, e.g., orientation inaccuracy due toinaccurate readings provided by a capture device, or devices. Expression(4) yields a weighted capture orientation, G_(θ) _(p) , and applies aweight to an orientation associated with a world location, as a possibleactual location of a point of interest captured by a device associatedwith a reported capture point, depending on how the location is orientedwith respect to the capture orientation, with larger weights applied tothe location(s) closer to the line of sight as compared to thelocation(s) that are farther away from the line of sight. In accordancewith one or more embodiments, the standard deviation σ_(θ) may beempirically determined and may reflect the variations in orientations,e.g., reported capture orientations. The standard deviation σ_(θ) may beimpacted by such factors as measurement accuracy of manufacture's, ormanufacturers', device(s), location in the world, etc.

In accordance with one or more embodiments, the numerator of theexponent in expression (4) is the square of the orientation angleα(u_(w), u_(p),θ_(p)), which angle may be determined in accordance withexpressions (1)-(3) and is the angle having the capture point as thevertex between the two equal sides of an isosceles triangle, e.g., thesides formed between the capture point u_(p)=(λ_(p),φ_(p)) and a worldpoint u_(w)=(λ_(w),φ_(w)) the capture point and a line of sight pointu_(o)=(λ_(o),λ_(φ)) such that δ(u_(w),u_(p))=δ(u_(o),u_(p)). Theorientation angle α(u_(w), u_(p),θ_(p)) may be considered to be adifference between the capture orientation and a world pointorientation. By way of a non-limiting example, assuming for the sake ofthe example that 60% of photos are within 5° of a mean captureorientation, using expression (4), a smaller value for angle α(u_(w),u_(p),θ_(p)) may result in a greater weight being applied; conversely, alarger angle α(u_(w), u_(p),θ_(p)) may result in a lesser weight beingapplied. By way of a further non-limiting example, in expression (4),σ_(θ) may have a stronger impact, or larger weight, for smaller valuesof angle α(u_(w), u_(p),θ_(p)), where the difference in the angles oforientation associated with the world and capture points is smaller,relative to a large angle α(u_(w), u_(p),θ_(p)), where the difference inthe angles of orientation associated with the world and capture pointsis larger.

FIG. 4 illustrates capture orientation modeling in accordance with oneor more embodiments of the present disclosure. In the example of FIG. 4,dashed line 408 represents a line of sight, e.g., a line of sightassociated with a reported capture location and orientation, at which anapplied weight may be the highest, e.g., 0.6%, such that the greatestweighting may be applied to a location with an orientation along theline of sight. Locations having an orientation falling within cone 402but having a different orientation than the line of sight andorientation 408 may have a weighting that is less than the line of sight408 weighting. By way of some further non-limiting examples, theweighting(s) associated with locations having an orientation fallingwithin cones 404A and 404B may have a weighting, e.g., 0.1%, which isless than the weighting(s) applied to locations having orientationswithin cone 402, and the weighting(s) associated with locations with anorientation falling within cones 406A and 406B may be less than theweighting(s) applied to locations having orientations within cones 402,404A and 404B.

In the example of FIG. 4, the cone 402 may vary fix differentmanufacturers and/or accuracies. The more likely that the reportedorientation is accurate, or alternatively stated the more accurate thedevice, the narrower cone 402, for example. Conversely and by way of afurther non-limiting example, the less likely the reported orientationis accurate, or alternatively stated the less accurate the device, thebroader cone 402. By way of yet a further non-limiting example, cone 402is narrower for a more accurate device relative to cone 402 for a lessaccurate device.

In accordance with one or more embodiments, location inaccuracies may beaddressed using a two-dimensional distribution, e.g., Gaussiandistribution. Of course, it should be apparent that other distributions,such as a Gamma, Rayleigh, Weibull, etc, distribution, may be used inconnection with one or more embodiments of the present disclosure. Inaccordance with one or more embodiments, the two-dimensionaldistribution has a standard deviation σ_(u) and is centered on u_(p). Inaccordance with one or more such embodiments, the model may be expressedas:

$\begin{matrix}{{G_{u_{p}}\left( {{u_{w};u_{p}},\sigma_{u}} \right)} = {\frac{1}{2{\pi\sigma}_{u}^{2}}{\mathbb{e}}^{- \frac{{\delta{({u_{w},u_{p}})}}^{2}}{2\sigma_{u}^{2}}}}} & {{Expr}.\mspace{11mu}(5)}\end{matrix}$

In expression (5), a weight is applied to each possible location, e.g.,each world location, for a point of interest depicted in a media itembased on how far away the location is from the capture location, suchthat larger weights may be applied for locations closer to the capturelocation than weights that may be applied for those locations fartheraway from the capture location. In accordance with one or moreembodiments, the standard deviation σ_(u) may be empirically determinedand may reflect the variations in locations, e.g., reported capturelocations. The standard deviation σ_(u) may be impacted by such factorsas measurement accuracy of manufacture's, or manufacturers′, device(s),location in the world, etc. In accordance with one or more embodiments,the numerator of the exponent in expression (5) is the square of thedistance δ(u_(w),u_(p)), which distance is a distance between captureand world point. By way of a non-limiting example, using expression (5),a smaller value for distance δ(u_(w),u_(p)) may result in a greaterweight being applied; conversely, a larger distance δ(u_(w),u_(p)) mayresult in a lesser weight being applied. By way of a furthernon-limiting example, in expression (5), σ_(θ) will have a strongerimpact, or larger weight, for smaller distances δ(u_(w),u_(p)), wherethe distance between the world and capture points is smaller, relativeto a large distance δ(u_(w),u_(p)), where the distance between the worldand capture points is larger.

FIG. 5 illustrates capture location modeling in accordance with one ormore embodiments of the present disclosure. In the example of FIG. 5, acenter 502, which may be a center point, may correspond to the meancapture location at which an applied weight is the highest, such thatthe greatest, or highest, weighting is applied to that location.Locations other than the mean capture location corresponding to center502 but within area 504 may have a weighting that is less than theweighting associated with center 502. By way of some furthernon-limiting examples, the weighting(s) associated with locations withinarea 506 may be less than the weighting(s) applied to locations withinarea 504, and the weighting(s) associated with locations within area 508may be less than the weighting(s) applied to locations within areas 502,504 and 506.

In the example of FIG. 5, the areas may vary for different manufacturersand/or accuracies. The more likely that the reported capture location isaccurate, or alternatively stated the more accurate the device, thesmaller area 504, for example. Conversely and by way of a furthernon-limiting example, the less likely the reported capture location isaccurate, or alternatively stated the less accurate the device, thebroader area 504. By way of a yet a further non-limiting example, area504 may be narrower for a more accurate device relative to area 504 fora less accurate device.

In accordance with one or more embodiments, weightings, or weights,G_(p) for a particular photo pεP may be obtained by convolving theorientation inaccuracy weights with the location inaccuracy weights,which may be represented as:G _(p) =G _(θ) _(p) *G _(u) _(p)   Expr. (6)

By virtue of expression (6), each location in the world may beinfluenced by both capture location and orientation inaccuracies. Inaccordance with one or more embodiments, for each world locationconsidered, the capture orientation inaccuracy weight G_(θ) _(p)associated with a photo pεP may be combined with the location inaccuracyweight G_(u) _(p) by multiplying the weight to generate the convolvedweight G_(p) for the photo. FIG. 6 provides an example of a modeling ofthe convolved weights in accordance with one or more embodiments of thepresent disclosure. In the example of FIG. 6, the capture orientationmodeling of FIG. 4 is modified by the location modeling of FIG. 5. Inthe example, the orientation is fixed to show the influence of locationinaccuracies on the orientation. Additionally and in the example, thegreatest convolved weight G_(p) is found within area 608, e.g., the areabounded by the smallest cone-shape. A convolved weight G_(p) outsidearea 608 but within in area 610 may be less than those weight(s) foundin area 608, and the convolved weight G_(p) outside area 610 but withinarea 612 may be less than those weight(s) associated with areas 610 and608.

As a result of the location inaccuracies being convolved with theorientation inaccuracies, locations that are really close to the capturelocation may actually receive lower weights relative to other locationsfarther away from the capture location. Such a result may occur due tothe location inaccuracies also considering the area behind a capturelocation when convolving world points close to the capture location,which due to their large orientation angle, θ_(p), have practically zeroweight; thus after convolution points near the capture location may havea lower weight than expected. In the example of FIG. 6, area 612, whichrepresents location(s) closer to the capture point than locationsrepresented by areas 608 and 610, has lower weights than areas 610 and608. By way of a non-limiting example, a weighting scheme thatassociates a lower weight with locations closer to the capture locationmay negatively impact such locations, and world locations of POIs mayactually receive low scores; such a result is logical when consideringthat the person, or persons, may actually have been standing somedistance in any direction from the position supplied by the GPS, forinstance, and as such the weights close to the capture location mayreflect this.

In accordance with one or more embodiments, weights for each photo pεPin collection P may be aggregated, or combined to obtain aggregate,final weights

_(p). In accordance with one or more such embodiments, for each worldlocation, the weights may be combined by summing the convolved weightG_(p) of each photo pεP in collection for the given world location,which may be expressed as:

$\begin{matrix}{{??}_{p} = {\sum\limits_{p \in P}G_{p}}} & {{Expr}.\mspace{11mu}(7)}\end{matrix}$

In so doing, in accordance with one or more embodiments, the world maybe modeled as a density map, where the weight associated with eachlocation in the world may reflect the likelihood of a point of interestbeing situated at that location. By way of a non-limiting example,assuming that each photo pεP of one or more collections P has anassociated set of weights G_(p) modeling each location in the world, theaggregate final weights

_(p) across all of the photos and world locations may be used to modelthe world as a density map, where the weight associated with eachlocation in the world reflects the likelihood of a point of interestbeing situated at that location, FIG. 7 provides an example of a densitymapping formed from aggregated convolved weights associated with anumber of photos in accordance with one or more embodiments of thepresent disclosure. In the example of FIG. 7, the density mapping may beused to determine a likelihood of a point of interest being present at agiven location. By way of a non-limiting example, each point in themapping may correspond to a given world location, and each point has anassociated probability, or likelihood. Point 702 has the best associatedprobability, or likelihood, of a point of interest being present at theworld location corresponding to point 702. By way of a furthernon-limiting example, each of points 704, 706 and 708 are associatedwith a lesser confidence than point 702 that a point of interest islocated at the corresponding world location.

By way of a non-limiting example, multiple local maxima may exist suchas and without limitation when processing photos known to depict aparticular POT, when a single photo is processed, when the lines ofsight of all photos do not intersect, when the lines of sight intersectin multiple locations, etc. In accordance with one or more embodiments,if multiple local maxima exist, various approaches may be used. By wayof a non-limiting example, a POI location may be estimated by, forexample, taking the average location of a collection of world pointsthat share the same maximum. By way of a further non-limiting example, aPOI location may be estimated by taking the average of the collection ofworld points with each world point in the collection being weighted byits underlying density. By way of a further non-limiting example, anestimate of a POI location may be determined by applying a filteringoperation that uses a threshold value such that weights that fall belowthe threshold value are set to zero and/or such that weights that areabove the threshold value are set equal to one. By setting weights thatfall below a threshold value to zero, any influence of such low-weightedlocations may be removed. By setting weights that are above thethreshold value to one, each of the high-scoring locations may beconsidered the same as the other high-scoring locations and theselocations may be modeled equally rather than giving one or more of thehigh-scoring locations more priority than others of the high-scoringlocations.

In accordance with one or more embodiments of the present disclosure,application of one of the orientation inaccuracy weighting and locationinaccuracy weighting may be optional, and such option might be exercisedat convolution. In a case that location inaccuracy weighting is optionalso that the location inaccuracy weighting is not to be applied,convolution uses the orientation inaccuracy weight without combining itwith the location inaccuracy weight. In a case that orientationinaccuracy weighting is optional so that the orientation inaccuracyweighting is not to be applied, convolution uses the location inaccuracyweight without combining it with the orientation inaccuracy weight. Byway of a non-limiting example, combined weights may be determinedthrough convolution using a “flat kernel” that applies a weight of “1”at a given orientation or location and “0” anywhere else. To furtherillustrate without limitation, the “flat kernel” may be aone-dimensional kernel for orientation inaccuracy weighting and atwo-dimensional kernel for location inaccuracy weighting. By way of anon-limiting example, a given orientation might be along the line ofsight identified by the capture orientation, and a given location may bethe capture location. By way of a further example, all points along theline of sight may be given a score of “1” and any points that are notalong the line of sight may be given a weight of “0”. It is possible touse an orientation range or a location range. By way of a non-limitingexample, an orientation range may be used such that additional points,e.g., points in addition to the points along the line of sight, may beconsidered; e.g., points along the line of sight and points within agiven range of the line of sight are given a score of “1” and any otherpoints are given a score of “0.”. To illustrate further withoutlimitation, the additional points mien be points that are within 5degrees, plus or minus, of the capture orientation. Making one of theweights, i.e., either the orientation or the location weight, optionalmay therefore result in output from convolution being the value of thenon-optional weight, e.g., the non-optional weight is not modified bythe optional weight. The score that is assigned to an inaccuracy weightmay be used to determine whether or not the inaccuracy weight isapplied, e.g., applied where the score is “1” and not applied where thescore is “0.”

Kernel, distribution and/or model may be used herein interchangeably.FIG. 11 provides examples of flat and Gaussian kernels in accordancewith one or more embodiments of the present disclosure. A kernel, modelor distribution may include, but is not limited to a flat kernel, suchas that depicted by kernel 1102, a Gaussian kernel, such as thatdepicted by kernel 1104, etc. Other examples of a kernel, model ordistribution include without limitation Weibull, Rayleigh, Gamma, etc.

In accordance with one or more embodiments, photos that are known to betaken of a specific point of interest, or points of interest, may beprocessed, and a given world location with the highest or greatestweight, which indicates the greatest likelihood or confidence, may beselected as an optimal estimate of a world location of a point ofinterest. By way of a non-limiting example, each photo might be selectedbased on metadata associated with the photo. Embodiments of the presentdisclosure may process any photos regardless of what is depicted in thephotos. By way of a non-limiting example and in such a case, a densitymap may be inspected for local maximum, e.g. using mean-shiftclustering, where each maxima may represent the point of interest.

Geographic, or world, locations may be represented as a bounded functionof continuous longitude and latitude values; however, such locations aretypically expressed in a discrete domain. Thus, in accordance with oneor more embodiments, a two-dimensional histogram may be used torepresent point of interest likelihoods, where the two dimensions mayrepresent longitude and latitude.

In accordance with one or more embodiments, geo-referenced and orientedmedia items captured close to each other may be accumulated, and bytracing the line of sight originating from each capturing device'slocation along its orientation, a most likely geographic position forone or more points of interest depicted by the media items may beinferred. Due to measurement inaccuracies associated with bothgeo-location and geo-orientation, one or more such embodiments of thepresent disclosure take such inaccuracies into account while estimatinga point of interest's location. More specifically, the differencebetween the actual capture location and orientation and a reportedlocation and orientation can be modeled according to one or moredistributions (e.g. Weibull, Rayleigh, Gamma, Gaussian, etc.distribution), which distribution(s) may be used to assign one or moreweights to each location in the world where such weight(s) may beconsidered to be the likelihood that the point of interest could havebeen seen by the capturing device. Doing this for all media items andsuperimposing the derived likelihoods, a density map may be acquiredwhere each local maximum, which local maximum might be determined assatisfying a threshold maximum, might be can be considered a location ofa point of interest. By way of a non-limiting example, a more popularpoint of interest might have more support than a lesser point ofinterest.

In accordance with one or more embodiments, each location, or point, inthe world receives an orientation inaccuracy weight for a media item,after which one or more location inaccuracy weight(s) may be identifiedfor the world location in connection with a given media item. Inaccordance with one or more such embodiments, a one-dimensional kernelmay be used to determine each orientation inaccuracy weight, and atwo-dimensional kernel may be used to determine the location inaccuracyweight(s). As discussed herein, either kernel may be a flat kernel oralternatively a statistical distribution, such statistical distributionmay be without limitation a Gaussian, or other, statisticaldistribution.

In accordance with one or more embodiments, while the two-dimensionalkernel used in location inaccuracy weighting may be of infinite size,practically speaking, weights are likely to be more meaningful near themiddle or center of the kernel where the weights are the highest. Assumefor the sake of example and without limitation that the kernel is aGaussian distribution, the weighting is highest at the center and dropsoff quickly moving away from the center. In a practical sense, thetwo-dimensional kernel used for the location inaccuracy weighting maynot need to cover all of the world locations. By way of a furtherillustration without limitation, where the world might be represented as6×3 million cells, the two-dimensional kernel used for locationinaccuracy weighting might cover 100×100 cells, or some other subset ofthe cells representing the entire world.

In accordance with one or more embodiments, conceptually speaking, thetwo-dimensional kernel may be placed at each world location so that themiddle of the kernel corresponds to a given world location and theremainder of the kernel corresponds to the remaining world locationscovered by the kernel. To further illustrate without limitation, whilethe kernel may cover all of the world locations, since the weightdecreases and ultimately drops off to zero the further away a worldlocation is from the center of the kernel, a world location that may becovered by the kernel but is a far enough away from the world locationat the center of the kernel may have little if any location inaccuracyweighting. At each world location covered by the kernel, an orientationvalue determined for the world location is multiplied by a locationinaccuracy weight identified for the world location using thetwo-dimensional kernel. By way of a non-limiting example, where aGaussian distribution is used for the two-dimensional kernel, the worldlocation at the center of the Gaussian distribution has the highestassociated location inaccuracy weight, locations directly adjacent tothe world location have associated location inaccuracy weights that aresomewhat less than the highest weight, and so on such that the locationinaccuracy weights of “neighboring” world locations decrease, andultimately reach zero, as the distance from the world location at thecenter of the kernel increases. For each world location covered by thetwo-dimensional kernel, its orientation inaccuracy weight may becombined, or convolved, with the world location's location inaccuracyweight determined using the two-dimensional kernel. To furtherillustrate without limitation, a convolved value for a world locationmay be determined by multiplying the world location's orientationinaccuracy weight by the location inaccuracy weight determined for theworld location using the two-dimensional location inaccuracy kernel, andwhere the world location is far enough away from the world location atthe center of the two-dimensional kernel, the location inaccuracy weightdetermined for the world location as a neighboring location may be zeroand the resulting convolved value may be zero.

In accordance with one or more embodiments, for each media item, eachworld location's density value may be determined by summing theconvolved values determined for the world location and its neighboringworld locations, and the two-dimensional kernel positioned over theworld location determines the location inaccuracy weight for the worldlocation at the center of the kernel as well as each of the other worldlocations covered by the kernel. Where multiple media items areconsidered, i.e., a media item collection comprises more than one mediaitem, the density value for a given world location may be the aggregateof the density values determined for the given world location for all ofthe media items in the collection.

FIG. 8, which comprises FIGS. 8A and 8B, provides an example processflow to assign weight(s) to world locations in accordance with one ormore embodiments of the present disclosure. In accordance with one ormore embodiments, in FIG. 8, orientation inaccuracy weights may becomputed independently and in parallel, location inaccuracy weights maybe applied after each world location's orientation weight(s) is/arecomputed, and orientation and location inaccuracies may be combinedthrough convolution. In accordance with one or more such embodiments,orientation inaccuracies are computed in a world space, such that eachlocation in the world receives a weight according to an orientationinaccuracy statistical distribution and a world location's orientationinaccuracy weight may be convolved with a location inaccuracy weightdetermined for the world location using a location inaccuracystatistical distribution.

In the non-limiting example shown in FIG. 8, for each media item in acollection of one or more media items, an orientation inaccuracy weightis determined for each world location, using steps 802, 804, 806, 808and 810; for each media item in the collection, a capture locationinaccuracy weighting statistical distribution may be used to determinelocation inaccuracy weights for world locations, a world location'slocation inaccuracy weight may be combined, or convolved, with the worldlocation's orientation inaccuracy weight, using steps 822, 828, 830, 832and 834; the current world point's convolved, or density, weight may becombined with density weights for “neighboring world points” to generatean aggregate density value (see FIG. 9B); for each media item in thecollection, an aggregate density value may be determined for the worldlocation across the media items in the collection, at step 824; and adensity map comprising the density values for each of the worldlocations may be used, at step 826, to estimate one or more POIlocations.

More particularly and with reference to FIG. 8A, at step 802, adetermination is made whether any media items in the collection of mediaitems being considered remain to be processed. In accordance with one ormore embodiments, the collection may comprise media items havingassociated capture points within a given one or more geographic areas.If not, processing continues at step 822 of FIG. 8B to take into accountcapture location inaccuracy weights associated with the world points.

If it is determined, at step 802, that media items in the collectionremain to be processed, processing continues at step 804 to get the nextmedia item. At step 806, a determination is made whether or not anyworld points remain to be processed for the current media item. If notprocessing continues at step 802 to determine whether or not any mediaitems remain to be processed. If it is determined, at step 806, thatworld points remain to be processed for the current media item,processing continues at step 808 to get the next world point. A captureorientation inaccuracy weight is determined at step 810. Processingcontinues at step 806 to process any remaining world points for thecurrent media item.

FIG. 9A provides a process flow example for use in determining a captureorientation inaccuracy weight in accordance with one or moreembodiments. At step 902, a point along the line of sight of the capturedevice capturing the media item is determined using the reported capturelocation and orientation of the capture device.

With reference to FIG. 3, the line of sight may be determined using thereported capture point 304. By way of a non-limiting example, thereported capture orientation, which may be relative to true north, isindicated by angle 318. In accordance with one or more embodiments, aline of sight point may be selected that has the same orientation as thereported capture orientation and in accordance with expression (3).Using expression (3), a point along the line of sight may be selectedsuch that the distance between the capture and line of sight points isthe same as the distance between the capture and the current worldpoints. In so doing, an isosceles triangle, e.g. triangle 300 of FIG. 3,is formed.

Referring again to FIG. 9A, the angles of the isosceles triangle formedfrom the reported capture point, the current world point and theselected line of sight point are determined. With reference to FIG. 3,angles 322 and 324 of triangle 300 may be determined using expression(2) and angle 326 of triangle 300 may be determined using expression(1). Referring again to FIG. 9A, at step 906, where an orientationinaccuracy weight is being determined, a statistical distribution may beused to determine the orientation inaccuracy weight for the currentmedia item given the current world point, capture point, which capturepoint includes the capture orientation, and an empirically-determinedstandard deviation. In accordance with one or more embodiments, thestatistical distribution is a one-dimensional distribution, such as andwithout limitation a Gaussian distribution, centered on a mean captureorientation with a standard deviation σ_(θ), which may be a measure ofhow much variation exists between reported capture orientations.

Referring again to FIG. 8A, at step 806, processing continues to processany remaining world points for which a capture orientation inaccuracyweight is to be determined. If it is determined, at step 806, that thereare no remaining world locations to be processed for the current mediaitem, processing continues at step 802 to process any remaining mediaitems in the collection. If it is determined that orientation inaccuracyweights have been determined for each of the media items in thecollection, processing continues at step 822 of FIG. 8B to determinelocation inaccuracy weights for world points in connection with eachmedia item in the collection, such that for each media item in thecollection a location inaccuracy weight is determined for world points.

Referring to FIG. 8B, at step 822, a determination is made whether eachof the media items in the collection has been processed, and if soprocessing continues at step 824. If it is determined, at step 822, thatone or more media items remain to be processed, processing continues atstep 828 to get the next media item in the collection. At step 830, adetermination is made whether any world locations remain to be processedfor the current media item. If it is determined, at step 830, that worldpoints remain to be processed, processing continues at step 832 to getthe next world point to be processed for the current media item. At step834, a capture location inaccuracy weight for the current world point isidentified using a capture location inaccuracy statistical modelcentered over the current world point. In accordance with one or moreembodiments, the capture location inaccuracy statistical model may be a“flat kernel.” The capture location inaccuracy statistical model may beused to identify a capture location inaccuracy weight that may becombined with the capture orientation inaccuracy weight to generate aconvolved weight for a world location.

In accordance with one or more embodiments, a statistical distributionmay be used to determine a capture location inaccuracy weight for thecurrent media item given the current world point, capture point, whichcapture point includes the capture orientation, and anempirically-determined standard deviation σ_(u). In accordance with oneor more embodiments, the statistical distribution may be atwo-dimensional distribution, such as and without limitation a Gaussiandistribution, centered on the capture point with a standard deviationσ_(u). In accordance with one or more embodiments, for a given mediaitem, a convolved weight for a given geographic location, orgeographical point, may be determined by combining capture orientationand location inaccuracy weights determined for the given geographiclocation and media item using expression (6). In accordance with one ormore embodiments, an aggregate of the combined capture orientation andlocation inaccuracies may be determined for a collection of media itemsin accordance with expression (7).

FIG. 9B provides a process flow example for use in identifying capturelocation inaccuracy and density weights in accordance with one or moreembodiments. Referring to FIG. 9B, at step 910, a location inaccuracystatistical distribution, or model, is centered over the current worldpoint. At step 912, a determination is made whether any world pointscovered by the distribution remain to be processed. If not, the processflow of FIG. 9B ends. If it is determined, at step 912, that one or moreworld points remain to be processed, processing continues at step 914 toget the next world point covered by the location inaccuracy statisticaldistribution. By way of a non-limiting example, the covered world pointmight be the current world point over which the statistical distributionis positioned or another world point covered by the statisticaldistribution centered on the current world point. At step 916, thecovered world point's location inaccuracy weight identified using thestatistical distribution is combined with the covered world point'sorientation inaccuracy to generate a convolved value for the coveredworld point.

At step 918, a density weight is determined for the current world pointover which the statistical distribution is centered by aggregating theconvolved values determined for each of the world points covered by thestatistical distribution centered over the current world point.Processing continues at step 912 to process any remaining world pointscovered by the distribution that is centered over the current worldpoint.

In accordance with one or more embodiments, a density value isdetermined for each world point in connection with each media item inthe collection, and all of the density values determined for a worldpoint may be aggregated to generate an aggregate density value for theworld point across all of media items in the collection. A density mapmay be formed comprising the aggregate density values of all of theworld points. With reference to FIG. 8B, step 824 may process each ofthe world locations to generate the aggregate density value for eachworld location. FIG. 10 provides a density value, or density weight,aggregation process flow in accordance with one or more embodiments ofthe present disclosure. In accordance with one or more embodiments, thesteps of FIG. 10 correspond to step 824 of FIG. 8B.

At step 1002, a determination is made whether any media items in thecollection, or plurality, of media items remain to be processed. If not,the process flow of FIG. 10 ends. If one or more media items remain tobe processed, processing continues at step 1004, at which a next mediaitem from the remaining media items is identified. At step 1006, adetermination is made whether any world locations remain to beprocessed. If not, processing continues at step 1002 to process anyremaining media items. If so, processing continues at step 1008 toidentify a next world point and the world point's density weighting,e.g., a combined weighting determined for media item and world point atstep 918 of FIG. 9B. At step 1010, the world point's weighting is addedto an aggregate weighting across the media items for the world point. Inaccordance with one or more embodiments, the aggregate weighting for theworld point reflects the world point's weightings determined for thecollection of media items. In accordance with one or more embodiments,one or more histograms, or density maps, may be formed using theaggregate weightings determined for a world points.

In accordance with one or more embodiments, media items depicting one ormore points of interest may be used to derive the location for one ormore points of interest. Each world point's aggregate weighting, whichmay be determined from the media items depicting the one or more pointsof interest, may be used to determine whether a point of interest islocated at the world point. By way of a non-limiting example, a worldpoint having an aggregate weighting that satisfies a threshold weightingmay be identified as a location of a point of interest.

In accordance with one or more embodiments, the media items may comprisea collection of media items identified as being in a given geographicarea, e.g. a media item's capture point is within the given geographicarea. A resulting local maxima in a likelihood density map may beidentified using mathematical techniques now known or later developed,where each local maxima, which may be a strong local maximum, might beconsidered a point of interest. One or more label(s) to associate witheach local maxima might be obtained by inspecting a description and/orfeature associated with the media items that contributed to the creationof that local maxima. The description/feature might take any form ortype, including without limitation textual, audio, video, image,multimedia, etc. In accordance with one or more embodiments, where alocation is determined for a point of interest, a geographic map mightbe used to associate a name from the geographic map as a representativename of the point of interest. By way of a non-limiting example, thelocation of the point of interest determined using one or more mediaitems capturing the point of interest may be used to locate the point ofinterest on the geographic map, and the name of the point of interestfrom the geographic map may be used as the representative name of thepoint of interest.

In accordance with one or more embodiments, the statisticaldistributions used as measurement inaccuracy distributions may be anytype of statistical distribution. A Gaussian distribution might beadvantageous as it provides a simplified representation of themeasurement inaccuracy distribution(s), while other statisticaldistributions, such as Gamma, Weibull or Rayleigh, might provide a moreaccurate representation of the measurement inaccuracy distributions(s).In accordance with one or more embodiments, the statisticaldistribution(s) used to represent the measurement inaccuracydistribution(s) might be dynamically identified. By way of anon-limiting example, a statistical distribution may be selected basedon a particular one or more locations in the world, and/or based onknown GPS/compass accuracies, which may be dependent on the number ofsatellites that are visible, the strength of the magnetic field of theearth, etc.

In accordance with one or more embodiments, altitude and verticalorientation of a capture device might be used for a more accurate lineof sight representation. By way of a non-limiting example, a terrain mapor blueprint of the geographic area, e.g., city, etc., might be used todetermine how far the line of sight might extend. By way of a furthernon-limiting example, a line of sight might be stopped if it isdetermined that the line of sight might be interrupted by a building orother obstacle to the line of sight, and a media item might be excludedfrom contributing to a determination of a location for a point ofinterest where it is determined that it would have been impossible forthe points of interest to be visible to the capturing device thatcaptured the media item.

Like capture inaccuracy weightings disclosed herein, other weights andconstraints may be applied to influence the likelihood of a point ofinterest being situated at each location in the world. By way of somenon-limiting examples, such additional weights and/or constraints may beassociated with altitude and inclination measurement(s), worldstructure, or layout, and focus distance measurement(s).

Altitude and inclination measurements, which may be available in capturemetadata, may be used to adjust a media item's density map by increasingor decreasing the likelihood of each world location that is considered.The weighting may be dependent on whether or not a point of interest ata given location is more or less likely to be visible in a scenecaptured by a device, e.g., a camera.

The structure of the world restricts the visibility of areas that areoccluded by elements, such as hills and buildings, or those that liebeyond the curvature of the earth. As a result, under these constraintsand given a capture location, orientation, altitude and inclination amedia item's density map may be modified to emphasize or deemphasize agiven one or more world locations.

A focus distance to the subject of focus in a captured scene may beincluded in the capture metadata depending on the capturing device usedto capture a media item, e.g., a photo. A focus distance associated withthe media item may be used to modify the media item's density map byaccentuating those locations within focus. Alternatively, the focusdistance may be estimated directly from the visual content using one ormore techniques, including techniques which may acquire knowledge of thedevice, the scene and/or information acquired from multiple shots of thesame scene with different device settings, or even modifications to thecamera lens itself. It should be apparent that embodiments of thepresent disclosure may use any techniques now known or later developedfor estimating focus distance.

As yet another non-limiting example, embodiments of the presentdisclosure may eliminate a media item, e.g., a photographs, which isfocused on a point of interest for which a location is not desired, suchas a non-stationary object, or point of interest. By way of anon-limiting example, a media item, such as photograph, may be analyzedto detect whether the media item's point of interest is an object forwhich it is not beneficial to determine a location, which object may bea non-stationary; and discard the media item from further consideration.By way of a further non-limiting example, a media item may be analyzedto detect a face, or faces, in focus within the visual content of aphoto, and discard any photo that is determined to be focused on aperson, or persons, rather than on a point, or points, of interest, fixwhich a location is to be determined. By way of a further non-limitingexample, a visual matching may be used, e.g., a media item may bevisually inspected to identify the point(s) of interest depicted by themedia item. The compass location may be adjusted accordingly to nolonger represent the line of sight, but rather the direction towards thepoint(s) of interest. In case more than one point of interest isdetected, multiple lines of sight might be used, and each one of themultiple lines of sight might originate from the same capture location.

FIG. 12 illustrates some components that can be used in connection withone or more embodiments of the present disclosure. In accordance withone or more embodiments of the present disclosure, one or more computingdevices, e.g., one or more servers, one or more user devices, one ormore digital media capture devices, etc., are configured to comprisefunctionality described herein. For example, a computing device 1202 canbe configured to execute program code, instructions, etc. to providefunctionality in accordance with one or more embodiments of the presentdisclosure. The same or another computing device 1202 can be configuredto capture one or more media items and/or associated capture points.

Computing device 1202 can serve content to user computing devices 1204using a browser application via a network 1206. Data store 1208 can beused to store program code to configure a server 1202 to executefunctionality in accordance with one or more embodiments of the presentdisclosure.

The user computing device 1204 can be any computing device, e.g., acomputing device capable of capturing media items and/or providingcapture point information. Computing device 1204 may include a devicesuch as and without limitation a personal computer, personal digitalassistant (PDA), wireless device, cell phone, internet appliance, mediaplayer, home theater system, and media center, or the like. For thepurposes of this disclosure a computing device includes a processor andmemory for storing and executing program code, data and software, andmay be provided with an operating system that allows the execution ofsoftware applications in order to manipulate data. A computing devicemay further comprise one or more components for capturing a media item,such as and without limitation a still and/or video image camera, and/orone or more components for determining a capture location, such as andwithout limitation a GPS component.

A computing device such as server 1202 and the user computing device1204 can include one or more processors, memory, a removable mediareader, network interface, display and interface, and one or more inputdevices, e.g., keyboard, keypad, mouse, etc. and input device interface,for example. One skilled in the art will recognize that server 1202 anduser computing device 1204 may be configured in many different ways andimplemented using many different combinations of hardware, software, orfirmware.

In accordance with one or more embodiments, a computing device 1202 canmake a user interface available to a user computing device 1204 via thenetwork 1206. The user interface made available to the user computingdevice 1204 can include content items, or identifiers (e.g., URLs)selected for the user interface in accordance with one or moreembodiments of the present invention. In accordance with one or moreembodiments, computing device 1202 makes a user interface available to auser computing device 1204 by communicating a definition of the userinterface to the user computing device 1204 via the network 1206. Theuser interface definition can be specified using any of a number oflanguages, including without limitation a markup language such asHypertext Markup Language, scripts, applets and the like. The userinterface definition can be processed by an application executing on theuser computing device 1204, such as a browser application, to output theuser interface on a display coupled, e.g., a display directly orindirectly connected, to the user computing device 1204.

In an embodiment the network 1206 may be the Internet, an intranet (aprivate version of the Internet), or any other type of network. Anintranet is a computer network allowing data transfer between computingdevices on the network. Such a network may comprise personal computers,mainframes, servers, network-enabled hard drives, and any othercomputing device capable of connecting to other computing devices via anintranet. An intranet uses the same Internet protocol suit as theInternet. Two of the most important elements in the suit are thetransmission control protocol (TCP) and the Internet protocol (IP).

As discussed, a network may couple devices so that communications may beexchanged, such as between a server computing device and a clientcomputing device or other types of devices, including between wirelessdevices coupled via a wireless network, for example. In accordance withone or more embodiments, a network may be used to transmit one or moremedia items and/or capture points.

A network may include mass storage, such as network attached storage(NAS), storage area network (SAN), or other forms of computer or machinereadable media, for example. A network may include the Internet, one ormore local area networks (LANs), one or more wide area networks (WANs),wire-line type connections, wireless type connections, or anycombination thereof. Likewise, sub-networks, such as may employdiffering architectures or may be compliant or compatible with differingprotocols, may interoperate within a larger network. Various types ofdevices may, for example, be made available to provide an interoperablecapability for differing architectures or protocols. As one illustrativeexample, a router may provide a link between otherwise separate andindependent LANs. A communication link or channel may include, forexample, analog telephone lines, such as a twisted wire pair, a coaxialcable, full or fractional digital lines including T1, T2, T3, or T4 typelines, Integrated Services Digital Networks (ISDNs), Digital SubscriberLines (DSLs), wireless links including satellite links, or othercommunication links or channels, such as may be known to those skilledin the art. Furthermore, a computing device or other related electronicdevices may be remotely coupled to a network, such as via a telephoneline or link, for example.

A wireless network may couple client devices with a network. A wirelessnetwork may employ stand-alone ad-hoc networks, mesh networks, WirelessLAN (WLAN) networks, cellular networks, or the like. A wireless networkmay further include a system of terminals, gateways, routers, or thelike coupled by wireless radio links, or the like, which may movefreely, randomly or organize themselves arbitrarily, such that networktopology may change, at times even rapidly. A wireless network mayfurther employ a plurality of network access technologies, includingLong Term Evolution (LTE), WLAN, Wireless Router (WR) mesh, or 2nd, 3rd,or 4th generation (2G, 3G, or 4G) cellular technology, or the like.Network access technologies may enable wide area coverage for devices,such as client devices with varying degrees of mobility, for example.For example, a network may enable RF or wireless type communication viaone or more network access technologies, such as Global System forMobile communication (GSM), Universal Mobile Telecommunications System(UMTS), General Packet Radio Services (GPRS), Enhanced Data GSMEnvironment (EDGE), 3GPP Long Term Evolution (LTE), LTE Advanced,Wideband Code Division Multiple Access (WCDMA), Bluetooth, 802.11b/g/n,or the like. A wireless network may include virtually any type ofwireless communication mechanism by which signals may be communicatedbetween devices, such as a client device or a computing device, betweenor within a network, or the like.

Signal packets communicated via a network, such as a network ofparticipating digital communication networks, may be compatible with orcompliant with one or more protocols. Signaling formats or protocolsemployed may include, for example, TCP/IP, UDP, DECnet, NetBEUI, IPX,Appletalk, or the like. Versions of the Internet Protocol (P) mayinclude IPv4 or IPv6. The Internet refers to a decentralized globalnetwork of networks. The Internet includes local area networks (LANs),wide area networks (WANs), wireless networks, or long haul publicnetworks that, for example, allow signal packets to be communicatedbetween LANs. Signal packets may be communicated between nodes of anetwork, such as, for example, to one or more sites employing a localnetwork address. A signal packet may, for example, be communicated overthe Internet from a user site via an access node coupled to theInternet. Likewise, a signal packet may be forwarded via network nodesto a target site coupled to the network via a network access node, forexample. A signal packet communicated via the Internet may, for example,be routed via a path of gateways, servers, etc. that may route thesignal packet in accordance with a target address and availability of anetwork path to the target address.

It should be apparent that embodiments of the present disclosure can beimplemented in a client-server environment such as that shown in FIG.12. Alternatively, embodiments of the present disclosure can beimplemented with other environments. As one non-limiting example, apeer-to-peer (or P2P) network may employ computing power or bandwidth ofnetwork participants in contrast with a network that may employdedicated devices, such as dedicated servers, for example; however, somenetworks may employ both as well as other approaches. A P2P network maytypically be used for coupling nodes via an ad hoc arrangement orconfiguration. A peer-to-peer network may employ some nodes capable ofoperating as both a “client” and a “server.”

FIG. 13 is a detailed block diagram illustrating an internalarchitecture of a computing device, e.g., a computing device such asserver 1202 or user computing device 1204, in accordance with one ormore embodiments of the present disclosure. As shown in FIG. 13,internal architecture 1300 includes one or more processing units,processors, or processing cores, (also referred to herein as CPUs) 1312,which interface with at least one computer bus 1302. Also interfacingwith computer bus 1302 are computer-readable medium, or media, 1306,network interface 1314, memory 1304, e.g., random access memory (RAM),run-time transient memory, read only memory (ROM), etc., media diskdrive interface 1320 as an interface for a drive that can read and/orwrite to media including removable media such as floppy, CD-ROM, DVD,etc. media, display interface 1310 as interface for a monitor or otherdisplay device, keyboard interface 1316 as interface for a keyboard,pointing device interface 1318 as an interface for a mouse or otherpointing device, and miscellaneous other interfaces not shownindividually, such as parallel and serial port interfaces, a universalserial bus (USB) interface, and the like.

Memory 1304 interfaces with computer bus 1302 so as to provideinformation stored in memory 1304 to CPU 1312 during execution ofsoftware programs such as an operating system, application programs,device drivers, and software modules that comprise program code, and/orcomputer-executable process steps, incorporating functionality describedherein, e.g., one or more of process flows described herein. CPU 1312first loads computer-executable process steps from storage, e.g., memory1304, computer-readable storage medium/media 1306, removable mediadrive, and/or other storage device. CPU 1312 can then execute the storedprocess steps in order to execute the loaded computer-executable processsteps. Stored data, e.g., data stored by a storage device, can beaccessed by CPU 1312 during the execution of computer-executable processsteps.

Persistent storage, e.g., medium/media 1306, can be used to store anoperating system and one or more application programs. Persistentstorage can also be used to store device drivers, such as one or more ofa digital camera driver, monitor driver, printer driver, scanner driver,or other device drivers, web pages, content files, playlists and otherfiles. Persistent storage can further include program modules and datafiles used to implement one or more embodiments of the presentdisclosure, e.g., listing selection module(s), targeting informationcollection module(s), and listing notification module(s), thefunctionality and use of which in the implementation of the presentdisclosure are discussed in detail herein.

For the purposes of this disclosure a computer readable medium storescomputer data, which data can include computer program code that isexecutable by a computer, in machine readable form. By way of example,and not limitation, a computer readable medium may comprise computerreadable storage media, for tangible or fixed storage of data, orcommunication media for transient interpretation of code-containingsignals. Computer readable storage media, as used herein, refers tophysical or tangible storage (as opposed to signals) and includeswithout limitation volatile and non-volatile, removable andnon-removable media implemented in any method or technology for thetangible storage of information such as computer-readable instructions,data structures, program modules or other data. Computer readablestorage media includes, but is not limited to, RAM, ROM, EPROM, EEPROM,flash memory or other solid state memory technology, CD-ROM, DVD, orother optical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other physical ormaterial medium which can be used to tangibly store the desiredinformation or data or instructions and which can be accessed by acomputer or processor.

Those skilled in the art will recognize that the methods and systems ofthe present disclosure may be implemented in many manners and as suchare not to be limited by the foregoing exemplary embodiments andexamples. In other words, functional elements being performed by singleor multiple components, in various combinations of hardware and softwareor firmware, and individual functions, may be distributed among softwareapplications at either the client or server or both. In this regard, anynumber of the features of the different embodiments described herein maybe combined into single or multiple embodiments, and alternateembodiments having fewer than, or more than, all of the featuresdescribed herein are possible. Functionality may also be, in whole or inpart, distributed among multiple components, in manners now known or tobecome known. Thus, myriad software/hardware/firmware combinations arepossible in achieving the functions, features, interfaces andpreferences described herein. Moreover, the scope of the presentdisclosure covers conventionally known manners for carrying out thedescribed features and functions and interfaces, as well as thosevariations and modifications that may be made to the hardware orsoftware or firmware components described herein as would be understoodby those skilled in the art now and hereafter.

While the system and method have been described in terms of one or moreembodiments, it is to be understood that the disclosure need not belimited to the disclosed embodiments. It is intended to cover variousmodifications and similar arrangements included within the spirit andscope of the claims, the scope of which should be accorded the broadestinterpretation so as to encompass all such modifications and similarstructures. The present disclosure includes any and all embodiments ofthe following claims.

The invention claimed is:
 1. A method comprising: selecting, using atleast one computing system, a plurality of digital media items, eachmedia item of the plurality having associated capture informationidentifying a location and orientation of a digital media devicecapturing the media item; generating, using the at least one computingsystem, a plurality of weights for each media item in the plurality,each weight of the media item's plurality of weights corresponding to ageographic location of a plurality of geographic locations, the weightreflects an estimated inaccuracy in the identified location andorientation of the digital media device and is generated usinginformation including the media item's identified location andorientation, the generated weight for use in determining whether a pointof interest depicted in the media item is located at the geographiclocation; and for each geographic location of the plurality,aggregating, using the at least one computing system, the plurality ofweights generated for the geographic location in connection with eachmedia item of the plurality of media items, an aggregated weight for agiven geographic location identifying a probability that at least onepoint of interest is located at the given geographic location.
 2. Themethod of claim 1, the generating further comprising: generating eachweight of the plurality of weights for a media item of the plurality ofmedia items as a combined weight for a geographic location of theplurality of geographic locations, comprising, for each geographiclocation of the plurality of geographic locations: generating, by the atleast one computing system, a capture orientation inaccuracy weight forthe media item using at least one statistical orientation inaccuracymodel; generating, by the at least one computing system, a capturelocation inaccuracy weight for the media item using at least onestatistical location inaccuracy model; and generating, by the at leastone computing system, the combined weight for the media item bycombining the capture orientation and location inaccuracies determinedfor the geographic location.
 3. The method of claim 2, wherein one ofthe capture orientation inaccuracy weight and the capture locationinaccuracy weight is optional and the other is non-optional, and thecombining resets the optional one of them so that the combined weightreflects only the non-optional one of the weights.
 4. The method ofclaim 2, the at least one statistical orientation inaccuracy modelcomprising at least one one-dimensional distribution centered on a meanorientation and having a standard deviation reflecting an orientationspread.
 5. The method of claim 4, for each geographic location u_(w) ofthe plurality of geographic locations, the generating a captureorientation inaccuracy weight for the media item using at least onestatistical orientation inaccuracy model further comprising the at leastone computing system: selecting a location along a line of sightindicated by the capture information's orientation, the line of sightlocation is selected so that a first distance between the geographiclocation u_(w) and the capture location u_(p) is the same as a seconddistance between the capture location u_(p) and the line of sightlocation; determining an orientation angle comprising an angle α(u_(w),u_(p),θ_(p)) representing an orientation angle between the capturelocation's orientation and the geographic location's orientation, themean orientation of the statistical orientation inaccuracy model isrepresented as θ_(p) and the standard deviation is represented as σ_(θ);and determining the capture orientation inaccuracy weight for the mediaitem and the geographic location u_(w) using the angle α(u_(w),u_(p),θ_(p)) and the standard deviation σ_(θ) in accordance with the atleast one statistical orientation inaccuracy model.
 6. The method ofclaim 5, the capture orientation inaccuracy weight for the media itemand the geographic location u_(w) is represented as G_(θ) _(p)(u_(w);u_(p),θ_(p),σ_(θ)), which is determined using an expression:${G_{\theta_{p}}\left( {{u_{w};u_{p}},\theta_{p},\sigma_{\theta}} \right)} = {\frac{1}{\sqrt{2\pi}\sigma_{\theta}}{{\mathbb{e}}^{- \frac{{\alpha{({u_{w},u_{p},\theta_{p}})}}^{2}}{2\sigma_{\theta}^{2}}}.}}$7. The method of claim 5, wherein the capture orientation inaccuracyweight for the media item and the geographic location u_(w) isdetermined using a statistical distribution for the at least onestatistical orientation inaccuracy model.
 8. The method of claim 2, theat least one statistical location inaccuracy model comprising at leastone two-dimensional model.
 9. The method of claim 8, for each geographiclocation u_(w) of the plurality of geographic locations, the generatinga capture location inaccuracy weight for the media item using at leastone statistical location inaccuracy model further comprising the atleast one computing system: determining a distance represented asδ(u_(w),u_(p)) between the capture location and the geographic location,the mean location of the statistical location inaccuracy model is thecapture location represented as u_(p); and determining the capturelocation inaccuracy weight for the media item and the geographiclocation u_(w) using the distance δ(u_(w),u_(p)) and the standarddeviation represented as σ_(u) in accordance with the at least onestatistical location inaccuracy model.
 10. The method of claim 9, thecapture location inaccuracy weight for the media item and the geographiclocation u_(w) is represented as G_(u) _(p) (u_(w);u_(p);σ_(u)) and isdetermined using an expression:${G_{u_{p}}\left( {{u_{w};u_{p}},\sigma_{u}} \right)} = {\frac{1}{2{\pi\sigma}_{u}^{2}}{{\mathbb{e}}^{- \frac{{\delta{({u_{w},u_{p}})}}^{2}}{2\sigma_{u}^{2}}}.}}$11. The method of claim 9, wherein the capture location inaccuracyweight for the media item and the geographic location u_(w) isdetermined using a statistical distribution for the at least onestatistical location inaccuracy model.
 12. A system comprising:processor; a storage medium for tangibly storing thereon program logicfor execution by the processor, the stored program logic comprising:selecting logic executed by the processor for selecting a plurality ofdigital media items, each media item of the plurality having associatedcapture information identifying a location and orientation of a digitalmedia device capturing the media item; generating logic executed by theprocessor for generating a plurality of weights for each media item inthe plurality, each weight of the media item's plurality of weightscorresponding to a geographic location of a plurality of geographiclocations, the weight reflects an estimated inaccuracy in the identifiedlocation and orientation of the digital media device and is generatedusing information including the media item's identified location andorientation, the generated weight for use in determining whether a pointof interest depicted in the media item is located at the geographiclocation; and aggregating logic executed by the processor foraggregating, for each geographic location of the plurality, use the atleast one computing system, the plurality of weights generated for thegeographic location in connection with each media item of the pluralityof media items, an aggregated weight for a given geographic locationidentifying a probability that at least one point of interest is locatedat the given geographic location.
 13. The system of claim 12, thegenerating logic executed by the processor for generating a plurality ofweights further comprising: generating logic executed by the processorfor generating each weight of the plurality of weights for the mediaitem of the plurality of media items as a combined weight for thegeographic location of the plurality of geographic locations, the logicfurther comprising, for each geographic location of the plurality ofgeographic locations: generating logic executed by the processor forgenerating a capture orientation inaccuracy weight for the media itemusing at least one statistical orientation inaccuracy model; generatinglogic executed by the processor for generating a capture locationinaccuracy weight for the media item using at least one statisticallocation inaccuracy model; and generating logic executed by theprocessor for generating the combined weight for the media item bycombining the capture orientation and location inaccuracies determinedfor the geographic location.
 14. The system of claim 13, wherein one ofthe capture orientation inaccuracy weight and the capture locationinaccuracy weight is optional and the other is non-optional, and thecombining resets the optional one of them so that the combined weightreflects only the non-optional one of the weights.
 15. The system ofclaim 13, the at least one statistical orientation inaccuracy modelcomprising at least one one-dimensional distribution centered on a meanorientation and having a standard deviation reflecting an orientationspread.
 16. The system of claim 15, for each geographic location u_(w)of the plurality of geographic locations, the generating logic executedby the processor for generating a capture orientation inaccuracy weightfor the media item using at least one statistical orientation inaccuracymodel further comprising: selecting logic executed by the processor forselecting a location along a line of sight indicated by the captureinformation's orientation, the line of sight location is selected sothat a first distance between the geographic location u_(w) and thecapture location u_(p) is the same as a second distance between thecapture location u_(p) and the line of sight location; determining logicexecuted by the processor for determining an orientation anglecomprising an angle α(u_(w),u_(p),θ_(p)) representing an orientationangle between the capture location's orientation and the geographiclocation's orientation, the mean orientation of the statisticalorientation inaccuracy model is represented as θ_(p) and the standarddeviation is represented as σ_(θ); and determining logic executed by theprocessor for determining the capture orientation inaccuracy weight forthe media item and the geographic location u_(w) using the angleα(u_(w),u_(p),θ_(p)) and the standard deviation σ_(θ) in accordance withthe at least one statistical orientation inaccuracy model.
 17. Thesystem of claim 16, the capture orientation inaccuracy weight for themedia item and the geographic location u_(w) is represented as G_(θ)_(p) (u_(w);u_(p),θ_(p),σ_(θ)), which is determined using an expression:${G_{\theta_{p}}\left( {{u_{w};u_{p}},\theta_{p},\sigma_{\theta}} \right)} = {\frac{1}{\sqrt{2\pi}\sigma_{\theta}}{{\mathbb{e}}^{- \frac{{\alpha{({u_{w},u_{p},\theta_{p}})}}^{2}}{2\sigma_{\theta}^{2}}}.}}$18. The system of claim 16, wherein the capture orientation inaccuracyweight for the media item and the geographic location u_(w) isdetermined using a statistical distribution for the at least onestatistical orientation inaccuracy model.
 19. The system of claim 13,the at least one statistical location inaccuracy model comprising atleast one two-dimensional model.
 20. The system of claim 19, for eachgeographic location u_(w) of the plurality of geographic locations, theinstructions to generate capture location inaccuracy weight for themedia item using at least one statistical location inaccuracy modelfurther comprising instructions to: determine a distance represented asδ(u_(w),u_(p)) between the capture location and the geographic location,the mean location of the statistical location inaccuracy model is thecapture location represented as u_(p); and determine the capturelocation inaccuracy weight for the media item and the geographiclocation u_(w) using the distance δ(u_(w),u_(p)) and the standarddeviation represented as σ_(u) in accordance with the at least onestatistical location inaccuracy model.
 21. The system of claim 20, thecapture location inaccuracy weight for the media item and the geographiclocation u_(w) is represented as G_(u) _(p) (u_(w);u_(p);σ_(u)) and isdetermined using an expression:${G_{u_{p}}\left( {u_{w};u_{p};\sigma_{u}} \right)} = {\frac{1}{2{\pi\sigma}_{u}^{2}}{{\mathbb{e}}^{- \frac{{\delta{({u_{w},u_{p}})}}^{2}}{2\sigma_{u}^{2}}}.}}$22. The system of claim 20, wherein the capture location inaccuracyweight for the media item and the geographic location u_(w) isdetermined using a statistical distribution for the at least onestatistical location inaccuracy model.
 23. A computer readablenon-transitory storage medium for tangibly storing thereon computerreadable instructions that when executed cause at least one processorto: select a plurality of digital media items, each media item of theplurality having associated capture information identifying a locationand orientation of a digital media device capturing the media item;generate a plurality of weights for each media item in the plurality,each weight of the media item's plurality of weights corresponding to ageographic location of a plurality of geographic locations, the weightreflects an estimated inaccuracy in the identified location andorientation of the digital media device and is generated usinginformation including the media item's identified location andorientation, the generated weight for use in determining whether a pointof interest depicted in the media item is located at the geographiclocation; and for each geographic location of the plurality,aggregating, use the at least one computing system, the plurality ofweights generated for the geographic location in connection with eachmedia item of the plurality of media items, an aggregated weight for agiven geographic location identifying a probability that at least onepoint of interest is located at the given geographic location.
 24. Thecomputer readable non-transitory storage medium of claim 23, theinstructions to generate further comprising instructions to: generateeach weight of the plurality of weights for a media item of theplurality of media items as a combined weight for a geographic locationof the plurality of geographic locations, the instructions furthercomprising, for each geographic location of the plurality of geographiclocations, instructions to: generate a capture orientation inaccuracyweight for the media item using at least one statistical orientationinaccuracy model; generate a capture location inaccuracy weight for themedia item using at least one statistical location inaccuracy model; andgenerate the combined weight for the media item by combining the captureorientation and location inaccuracies determined for the geographiclocation.
 25. The computer readable non-transitory storage medium ofclaim 24, wherein one of the capture orientation inaccuracy weight andthe capture location inaccuracy weight is optional and the other isnon-optional, and the combining resets the optional one of them so thatthe combined weight reflects only the non-optional one of the weights.26. The computer readable non-transitory storage medium of claim 24, theat least one statistical orientation inaccuracy model comprising atleast one one-dimensional distribution centered on a mean orientationand having a standard deviation reflecting an orientation spread. 27.The computer readable non-transitory storage medium of claim 26, foreach geographic location u_(w) of the plurality of geographic locations,the instructions to generate a capture orientation inaccuracy weight forthe media item using at least one statistical orientation inaccuracymodel father comprising instructions to: select a location along a lineof sight indicated by the capture information's orientation, the line ofsight location is selected so that a first distance between thegeographic location u_(w) and the capture location u_(p) is the same asa second distance between the capture location u_(p) and the line ofsight location; determine an orientation angle comprising an angleα(u_(w), u_(p),θ_(p)) representing an orientation angle between thecapture location's orientation and the geographic location'sorientation, the mean orientation of the statistical orientationinaccuracy model is represented as θ_(p) and the standard deviation isrepresented as σ_(θ); and determine the capture orientation inaccuracyweight for the media item and the geographic location u_(w) using theangle α(u_(w), u_(p),θ_(p)) and the standard deviation σ_(θ) inaccordance with the at least one statistical orientation inaccuracymodel.
 28. The computer readable non-transitory storage medium of claim27, the capture orientation inaccuracy weight for the media item and thegeographic location u_(w) is represented as G_(θ) _(p)(u_(w);u_(p),θ_(p),σ_(θ)), which is determined using an expression:${G_{\theta_{p}}\left( {{u_{w};u_{p}},\theta_{p},\sigma_{\theta}} \right)} = {\frac{1}{\sqrt{2\pi}\sigma_{\theta}}{{\mathbb{e}}^{- \frac{{\alpha{({u_{w},u_{p},\theta_{p}})}}^{2}}{2\sigma_{\theta}^{2}}}.}}$29. The computer readable non-transitory storage medium of claim 27,wherein the capture orientation inaccuracy weight for the media item andthe geographic location u_(w) is determined using a statisticaldistribution for the at least one statistical orientation inaccuracymodel.
 30. The computer readable non-transitory storage medium of claim24, the at least one statistical location inaccuracy model comprising atleast one two-dimensional model.
 31. The computer readablenon-transitory storage medium of claim 30, for each geographic locationu_(w) of the plurality of geographic locations, the instructions togenerate capture location inaccuracy weight for the media item using atleast one statistical location inaccuracy model further comprisinginstructions to: determine a distance represented as δ(u_(w),u_(p))between the capture location and the geographic location, the meanlocation of the statistical location inaccuracy model is the capturelocation represented as u_(p) and the standard deviation is representedas σ_(u); and determine the capture location inaccuracy weight for themedia item and the geographic location u_(w) using the distanceδ(u_(w),u_(p)) and the standard deviation represented as σ_(u) inaccordance with the at least one statistical location inaccuracy model.32. The computer readable non-transitory storage medium of claim 31, thecapture location inaccuracy weight for the media item and the geographiclocation u_(w) is represented as G_(u) _(p) (u_(w);u_(p);σ_(u)) and isdetermined using an expression:${G_{u_{p}}\left( {u_{w};u_{p};\sigma_{u}} \right)} = {\frac{1}{2{\pi\sigma}_{u}^{2}}{{\mathbb{e}}^{- \frac{{\delta{({u_{w},u_{p}})}}^{2}}{2\sigma_{u}^{2}}}.}}$33. The computer readable non-transitory storage medium of claim 31,wherein the capture location inaccuracy weight for the media item andthe geographic location u_(w) is determined using a statisticaldistribution for the at least one statistical location inaccuracy model.